Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities

 

 

 

Topics covered in this course: Statistical analysis and optimization of data

The course has two central parts

  1. Statistical analysis and optimization of data
  2. Machine learning
These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms

The following topics will be covered

  • Basic concepts, expectation values, variance, covariance, correlation functions and errors;
  • Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
  • Central elements of Bayesian statistics and modeling;
  • Gradient methods for data optimization,
  • Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;
  • Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;
  • Principal Component Analysis (PCA) and its mathematical foundation